require(data.table)
require(lubridate)
require(forecast)
require(skimr)
require(repr)
require(readxl)
require(ggplot2)

Zorunlu paket yükleniyor: data.table

Zorunlu paket yükleniyor: lubridate


Attaching package: ‘lubridate’


The following objects are masked from ‘package:data.table’:

    hour, isoweek, mday, minute, month, quarter, second, wday, week,
    yday, year


The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union


Zorunlu paket yükleniyor: forecast

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 

Zorunlu paket yükleniyor: skimr

Zorunlu paket yükleniyor: repr

Zorunlu paket yükleniyor: readxl

Zorunlu paket yükleniyor: ggplot2

house_index_data <- read_excel("Desktop/360R/house_price_index.xlsx")
house_index_data <- house_index_data[31:152, ]
house_index_data <- house_index_data[,-3]
colnames(house_index_data) <- c("Date", "House Price Index")
house_index_data$Date <- as.Date(paste(house_index_data$Date, "-01", sep = ""))
house_index_data$"House Price Index" <- as.numeric(house_index_data$"House Price Index")
initial_time <- c(year(min(house_index_data$Date)), month(min(house_index_data$Date)))
house_index_ts <- ts(house_index_data$"House Price Index", start = initial_time, frequency = 12)
house_index_ts

New names:
• `` -> `...3`

A Time Series: 11 × 12

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
2014	64.1	64.8	65.7	66.8	67.5	68.2	69.3	70.2	70.9	71.5	72.4	73.1
2015	74.2	75.4	76.6	77.7	78.9	79.9	80.6	81.1	81.7	82.7	83.7	84.5
2016	85.7	86.6	87.5	88.6	89.7	90.5	91.3	92.3	92.8	93.5	94.0	94.8
2017	95.5	96.7	97.9	98.7	99.8	100.3	100.5	100.8	101.4	102.2	102.8	103.4
2018	104.1	104.8	105.7	107.1	108.6	109.1	109.3	108.7	107.8	108.5	109.2	108.0
2019	107.6	108.6	109.0	109.3	110.2	111.0	112.4	114.4	115.2	116.0	117.1	118.8
2020	120.9	123.7	125.4	127.5	135.9	139.5	141.4	144.4	146.7	149.9	152.2	154.9
2021	157.7	161.7	165.5	168.8	175.4	180.3	185.4	192.7	198.9	210.2	229.2	247.4
2022	279.9	318.0	347.7	383.0	431.1	470.0	507.8	548.2	575.0	605.8	629.2	662.7
2023	708.5	768.1	809.6	847.4	878.0	921.0	988.7	1043.1	1087.9	1129.5	1150.0	1163.3
2024	1190.0	1213.0

M1_data <- read_excel("Desktop/360R/M1.xlsx")
M1_data <- M1_data[29:150, ]
M1_data <- M1_data[,-3]
colnames(M1_data) <- c("Date", "M1")
M1_data$Date <- as.Date(paste(M1_data$Date, "-01", sep = ""))
M1_data$"M1" <- as.numeric(M1_data$"M1")
initial_time <- c(year(min(M1_data$Date)), month(min(M1_data$Date)))
M1_ts <- ts(M1_data$"M1", start = initial_time, frequency = 12)
M1_ts

New names:
• `` -> `...3`

A Time Series: 11 × 12

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
2014	222778962	221554826	229538298	225530537	224382058	238150238	247677613	241878967	249785641	247424552	245494451	251991723
2015	251419161	256672749	261935572	273990507	280246416	285907662	294157799	302536632	322378630	308090197	307160179	312005973
2016	307780794	312767158	315704986	322480264	325211114	336562968	344856750	343756071	347747515	348794511	359751417	382351496
2017	376732225	377212027	401173247	419127390	411475365	421573145	419416200	433232693	434181238	429971761	437243382	449631796
2018	428232624	433707515	451583613	463082040	496312574	513594608	509485823	588067823	559184535	523005315	488062659	512524057
2019	505911428	517326639	569489652	567678259	612272035	608264637	602826683	641486805	655504373	666800207	677077915	712832597
2020	736761246	787440698	847047260	1004180564	1049003890	1101587027	1195463719	1212171623	1230939243	1274992753	1256046133	1219863695
2021	1196836433	1174517579	1221726118	1274480359	1339713644	1375019253	1414025729	1413801938	1461023866	1566529670	1873167065	2097220269
2022	2117864069	2184427231	2307772436	2382053021	2539199585	2662097003	2739907121	2775476404	2857946610	2950589658	2987390833	3131103033
2023	3173757166	3250031541	3469728070	3672319062	3523242342	4180536954	4390129243	4545752925	4492359898	4565790908	4631462146	4757514054
2024	4837221517	5049627766

unemployment_rate_data <- read_excel("Desktop/360R/unemployment_rate.xlsx")
unemployment_rate_data <- unemployment_rate_data[1:122, ]
unemployment_rate_data <- unemployment_rate_data[,-3]
colnames(unemployment_rate_data) <- c("Date", "Rate")
unemployment_rate_data$Date <- as.Date(paste(unemployment_rate_data$Date, "-01", sep = ""))
unemployment_rate_data$"Rate" <- as.numeric(unemployment_rate_data$"Rate")
initial_time <- c(year(min(unemployment_rate_data$Date)), month(min(unemployment_rate_data$Date)))
unemployment_rate_ts <- ts(unemployment_rate_data$"Rate", start = initial_time, frequency = 12)
unemployment_rate_ts

New names:
• `` -> `...3`

A Time Series: 11 × 12

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
2014	10.5	10.5	9.4	8.8	8.4	9.0	9.7	10.1	10.1	10.9	10.4	10.8
2015	11.9	11.7	10.4	9.6	8.9	9.6	10.0	10.0	10.3	10.5	10.2	10.9
2016	11.5	11.0	10.1	9.1	9.0	10.1	11.0	11.0	11.8	11.9	11.6	12.8
2017	13.5	12.9	11.7	10.5	9.7	10.4	10.6	10.6	10.4	10.5	10.0	10.3
2018	11.0	10.8	9.8	9.3	9.4	10.2	10.7	11.1	11.4	11.7	12.0	13.3
2019	15.1	15.0	13.9	12.9	12.5	13.4	14.1	14.2	14.0	13.3	12.9	13.6
2020	14.1	13.5	13.0	12.8	12.6	13.0	14.6	13.0	12.4	12.8	12.9	13.0
2021	13.4	14.1	13.1	12.9	12.4	10.4	12.1	12.0	11.1	10.7	10.9	11.3
2022	12.1	11.4	11.4	10.6	10.1	9.7	10.6	9.8	9.9	9.9	9.9	10.4
2023	10.3	10.7	10.2	10.0	8.8	9.0	9.7	9.2	8.9	8.2	8.8	8.9
2024	9.8	9.7

Needed data manipulation for independent variables:

satılık_ev_data = fread("Desktop/360R/satılık_ev.csv")
colnames(satılık_ev_data) <- c("Date", "Amount")
satılık_ev_data$Date <- as.Date(paste(satılık_ev_data$Date, "-01", sep = ""))
satılık_ev_data <- satılık_ev_data[satılık_ev_data$Date >= as.Date("2014-01-01") & satılık_ev_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(satılık_ev_data$Date)), month(min(satılık_ev_data$Date)))
satılık_ev_ts <- ts(satılık_ev_data$Amount, start = initial_time, frequency = 12)

konut_kredisi_data = fread("Desktop/360R/konut_kredisi.csv")
colnames(konut_kredisi_data) <- c("Date", "Amount")
konut_kredisi_data$Date <- as.Date(paste(konut_kredisi_data$Date, "-01", sep = ""))
konut_kredisi_data <- konut_kredisi_data[konut_kredisi_data$Date >= as.Date("2014-01-01") & konut_kredisi_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(konut_kredisi_data$Date)), month(min(konut_kredisi_data$Date)))
konut_kredisi_ts <- ts(konut_kredisi_data$Amount, start = initial_time, frequency = 12)

faiz_data = fread("Desktop/360R/faiz.csv")
colnames(faiz_data) <- c("Date", "Amount")
faiz_data$Date <- as.Date(paste(faiz_data$Date, "-01", sep = ""))
faiz_data <- faiz_data[faiz_data$Date >= as.Date("2014-01-01") & faiz_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(faiz_data$Date)), month(min(faiz_data$Date)))
faiz_ts <- ts(faiz_data$"Amount", start = initial_time, frequency = 12)

enflasyon_data = fread("Desktop/360R/enflasyon.csv")
colnames(enflasyon_data) <- c("Date", "Amount")
enflasyon_data$Date <- as.Date(paste(enflasyon_data$Date, "-01", sep = ""))
enflasyon_data <- enflasyon_data[enflasyon_data$Date >= as.Date("2014-01-01") & enflasyon_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(enflasyon_data$Date)), month(min(enflasyon_data$Date)))
enflasyon_ts <- ts(enflasyon_data$Amount, start = initial_time, frequency = 12)

darphane_data = fread("Desktop/360R/darphane.csv")
colnames(darphane_data) <- c("Date", "Amount")
darphane_data$Date <- as.Date(paste(darphane_data$Date, "-01", sep = ""))
darphane_data <- darphane_data[darphane_data$Date >= as.Date("2014-01-01") & darphane_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(darphane_data$Date)), month(min(darphane_data$Date)))
darphane_ts <- ts(darphane_data$Amount, start = initial_time, frequency = 12)

altın_data = fread("Desktop/360R/altın.csv")
colnames(altın_data) <- c("Date", "Amount")
altın_data$Date <- as.Date(paste(altın_data$Date, "-01", sep = ""))
altın_data <- altın_data[altın_data$Date >= as.Date("2014-01-01") & altın_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(altın_data$Date)), month(min(altın_data$Date)))
altın_ts <- ts(altın_data$Amount, start = initial_time, frequency = 12)

salgın_hastalık_data = fread("Desktop/360R/salgın_hastalık.csv")
colnames(salgın_hastalık_data) <- c("Date", "Amount")
salgın_hastalık_data$Date <- as.Date(paste(salgın_hastalık_data$Date, "-01", sep = ""))
salgın_hastalık_data <- salgın_hastalık_data[salgın_hastalık_data$Date >= as.Date("2014-01-01") & salgın_hastalık_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(salgın_hastalık_data$Date)), month(min(salgın_hastalık_data$Date)))
salgın_hastalık_ts <- ts(salgın_hastalık_data$Amount, start = initial_time, frequency = 12)

işsizlik_sigortası_data = fread("Desktop/360R/işsizlik_sigortası.csv")
colnames(işsizlik_sigortası_data) <- c("Date", "Amount")
işsizlik_sigortası_data$Date <- as.Date(paste(işsizlik_sigortası_data$Date, "-01", sep = ""))
işsizlik_sigortası_data <- işsizlik_sigortası_data[işsizlik_sigortası_data$Date >= as.Date("2014-01-01") & işsizlik_sigortası_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(işsizlik_sigortası_data$Date)), month(min(işsizlik_sigortası_data$Date)))
işsizlik_sigortası_ts <- ts(işsizlik_sigortası_data$Amount, start = initial_time, frequency = 12)

iş_ilanları_data = fread("Desktop/360R/iş_ilanları.csv")
colnames(iş_ilanları_data) <- c("Date", "Amount")
iş_ilanları_data$Date <- as.Date(paste(iş_ilanları_data$Date, "-01", sep = ""))
iş_ilanları_data <- iş_ilanları_data[iş_ilanları_data$Date >= as.Date("2014-01-01") & iş_ilanları_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(iş_ilanları_data$Date)), month(min(iş_ilanları_data$Date)))
iş_ilanları_ts <- ts(iş_ilanları_data$Amount, start = initial_time, frequency = 12)

işsizlik_data = fread("Desktop/360R/işsizlik.csv")
colnames(işsizlik_data) <- c("Date", "Amount")
işsizlik_data$Date <- as.Date(paste(işsizlik_data$Date, "-01", sep = ""))
işsizlik_data <- işsizlik_data[işsizlik_data$Date >= as.Date("2014-01-01") & işsizlik_data$Date <= as.Date("2024-02-01")]
initial_time <- c(year(min(işsizlik_data$Date)), month(min(işsizlik_data$Date)))
işsizlik_ts <- ts(işsizlik_data$Amount, start = initial_time, frequency = 12)

autoplot(satılık_ev_ts)
autoplot(konut_kredisi_ts)
autoplot(faiz_ts)
autoplot(enflasyon_ts)
autoplot(darphane_ts)
autoplot(altın_ts)
autoplot(salgın_hastalık_ts)
autoplot(işsizlik_sigortası_ts)
autoplot(iş_ilanları_ts)

Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'satılık_ev_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'satılık_ev_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'satılık_ev_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'satılık_ev_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'satılık_ev_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'satılık_ev_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'satılık_ev_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'satılık_ev_ts' in 'mbcsToSbcs': dot substituted for <b1>”

Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'altın_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'altın_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'altın_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'altın_ts' in 'mbcsToSbcs': dot substituted for <b1>”

Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'salgın_hastalık_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'salgın_hastalık_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'salgın_hastalık_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'salgın_hastalık_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'salgın_hastalık_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'salgın_hastalık_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'salgın_hastalık_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'salgın_hastalık_ts' in 'mbcsToSbcs': dot substituted for <b1>”

Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'işsizlik_sigortası_ts' in 'mbcsToSbcs': dot substituted for <c5>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'işsizlik_sigortası_ts' in 'mbcsToSbcs': dot substituted for <9f>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'işsizlik_sigortası_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'işsizlik_sigortası_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'işsizlik_sigortası_ts' in 'mbcsToSbcs': dot substituted for <c5>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'işsizlik_sigortası_ts' in 'mbcsToSbcs': dot substituted for <9f>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'işsizlik_sigortası_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'işsizlik_sigortası_ts' in 'mbcsToSbcs': dot substituted for <b1>”

Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'iş_ilanları_ts' in 'mbcsToSbcs': dot substituted for <c5>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'iş_ilanları_ts' in 'mbcsToSbcs': dot substituted for <9f>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'iş_ilanları_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'iş_ilanları_ts' in 'mbcsToSbcs': dot substituted for <b1>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'iş_ilanları_ts' in 'mbcsToSbcs': dot substituted for <c5>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'iş_ilanları_ts' in 'mbcsToSbcs': dot substituted for <9f>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'iş_ilanları_ts' in 'mbcsToSbcs': dot substituted for <c4>”
Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
“conversion failure on 'iş_ilanları_ts' in 'mbcsToSbcs': dot substituted for <b1>”

House Price Index Amount Model:

autoplot(house_index_ts) + ggtitle("House Price Index vs Time") + xlab("Year") + ylab("House Price Index")

ggAcf(house_index_ts, lag.max = 120)
ggAcf(house_index_ts)

df_house_index <- cbind(house_index_data[,-1], satılık_ev_data$Amount, konut_kredisi_data$Amount, faiz_data$Amount)
df_house_index

A data.frame: 122 × 4

House Price Index	satılık_ev_data$Amount	konut_kredisi_data$Amount	faiz_data$Amount
<dbl>	<int>	<int>	<int>
64.1	23	9	19
64.8	23	9	19
65.7	21	7	15
66.8	21	8	17
67.5	22	8	16
68.2	22	8	16
69.3	22	9	15
70.2	26	10	15
70.9	24	10	15
71.5	24	8	13
72.4	25	8	15
73.1	22	8	16
74.2	23	9	17
75.4	29	11	19
76.6	29	10	17
77.7	27	8	15
78.9	25	8	13
79.9	24	8	14
80.6	29	7	13
81.1	31	9	15
81.7	26	8	16
82.7	25	7	14
83.7	27	11	21
84.5	27	9	22
85.7	27	8	17
86.6	31	9	18
87.5	33	9	21
88.6	33	12	23
89.7	32	10	21
90.5	29	10	19
⋮	⋮	⋮	⋮
198.9	45	15	54
210.2	43	21	58
229.2	45	21	75
247.4	47	20	80
279.9	46	16	58
318.0	46	16	47
347.7	49	16	57
383.0	48	15	46
431.1	55	44	70
470.0	50	21	58
507.8	50	16	57
548.2	47	14	52
575.0	40	15	57
605.8	35	12	51
629.2	32	11	54
662.7	35	13	50
708.5	47	57	62
768.1	53	17	46
809.6	43	16	56
847.4	38	14	51
878.0	34	14	54
921.0	37	14	100
988.7	44	17	88
1043.1	41	17	76
1087.9	35	17	96
1129.5	30	14	83
1150.0	27	11	81
1163.3	28	10	94
1190.0	29	12	73
1213.0	26	13	65

require(GGally)
ggpairs(df_house_index)

Zorunlu paket yükleniyor: GGally

Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2

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tslm_house_index_1 <- tslm(house_index_ts ~ satılık_ev_ts + konut_kredisi_ts + faiz_ts)
tslm_house_index_2 <- tslm(house_index_ts ~ konut_kredisi_ts + faiz_ts)
tslm_house_index_3 <- tslm(house_index_ts ~ faiz_ts)

summary(tslm_house_index_1)
summary(tslm_house_index_2)
summary(tslm_house_index_3)

Call:
tslm(formula = house_index_ts ~ satılık_ev_ts + konut_kredisi_ts + 
    faiz_ts)

Residuals:
    Min      1Q  Median      3Q     Max 
-564.78 -110.08   10.81   49.84  675.45 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -96.4869    67.6589  -1.426    0.156    
satılık_ev_ts      1.4166     2.1740   0.652    0.516    
konut_kredisi_ts  -8.8782     2.1122  -4.203 5.14e-05 ***
faiz_ts           10.9634     0.9442  11.612  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 206.2 on 118 degrees of freedom
Multiple R-squared:  0.5401,	Adjusted R-squared:  0.5284 
F-statistic: 46.19 on 3 and 118 DF,  p-value: < 2.2e-16

Call:
tslm(formula = house_index_ts ~ konut_kredisi_ts + faiz_ts)

Residuals:
    Min      1Q  Median      3Q     Max 
-578.20 -109.87   10.74   47.25  662.13 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -61.5598    41.1878  -1.495    0.138    
konut_kredisi_ts  -8.0424     1.6741  -4.804 4.58e-06 ***
faiz_ts           11.0305     0.9363  11.781  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 205.7 on 119 degrees of freedom
Multiple R-squared:  0.5384,	Adjusted R-squared:  0.5307 
F-statistic:  69.4 on 2 and 119 DF,  p-value: < 2.2e-16

Call:
tslm(formula = house_index_ts ~ faiz_ts)

Residuals:
    Min      1Q  Median      3Q     Max 
-558.21 -127.84   14.37   57.71  739.54 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -122.5224    42.6369  -2.874   0.0048 ** 
faiz_ts        9.1690     0.9274   9.887   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 223.8 on 120 degrees of freedom
Multiple R-squared:  0.4489,	Adjusted R-squared:  0.4443 
F-statistic: 97.74 on 1 and 120 DF,  p-value: < 2.2e-16

CV_data <- data.frame(rbind(CV(tslm_house_index_1), CV(tslm_house_index_2), CV(tslm_house_index_3)))
CV_data

A data.frame: 3 × 5

CV	AIC	AICc	BIC	AdjR2
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
46913.56	1306.135	1306.652	1320.155	0.5283751
46204.49	1304.573	1304.915	1315.789	0.5306556
51667.92	1324.199	1324.402	1332.611	0.4443005

autoplot(house_index_ts, series = "Actual Data") + 
  autolayer(fitted(tslm_house_index_2), series = "Model Result")

checkresiduals(tslm_house_index_1)
checkresiduals(tslm_house_index_2)
checkresiduals(tslm_house_index_3)

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 97.864, df = 24, p-value = 6.935e-11

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 97.904, df = 24, p-value = 6.826e-11

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 105.46, df = 24, p-value = 3.47e-12

df_house_index[,"Residuals"] <- as.numeric(residuals(tslm_house_index_2))
ggplot(df_house_index, aes(x = fitted(tslm_house_index_2), y = Residuals)) + geom_point()

Don't know how to automatically pick scale for object of type <ts>. Defaulting
to continuous.

Money Supply Model:

autoplot(M1_ts) + ggtitle("Money Supply vs Time") + xlab("Year") + ylab("Money Supply")

ggAcf(M1_ts, lag.max = 120)
ggAcf(M1_ts)

df_M1 <- cbind(M1_data[,-1], faiz_data$Amount, enflasyon_data$Amount, darphane_data$Amount, altın_data$Amount)
df_M1

A data.frame: 122 × 5

M1	faiz_data$Amount	enflasyon_data$Amount	darphane_data$Amount	altın_data$Amount
<dbl>	<int>	<int>	<int>	<int>
222778962	19	9	7	12
221554826	19	5	8	10
229538298	15	4	7	9
225530537	17	5	9	8
224382058	16	5	9	7
238150238	16	5	9	8
247677613	15	5	10	7
241878967	15	3	9	7
249785641	15	3	10	7
247424552	13	4	11	6
245494451	15	4	10	9
251991723	16	5	8	8
251419161	17	9	9	10
256672749	19	6	9	10
261935572	17	6	10	8
273990507	15	5	9	8
280246416	13	7	7	6
285907662	14	9	6	7
294157799	13	7	7	8
302536632	15	4	6	10
322378630	16	4	6	7
308090197	14	4	7	6
307160179	21	6	11	9
312005973	22	9	8	7
307780794	17	13	6	7
312767158	18	5	9	10
315704986	21	5	8	8
322480264	23	5	7	7
325211114	21	5	7	9
336562968	19	5	6	8
⋮	⋮	⋮	⋮	⋮
1461023866	54	17	16	25
1566529670	58	25	14	38
1873167065	75	38	19	65
2097220269	80	56	19	100
2117864069	58	66	21	44
2184427231	47	38	21	47
2307772436	57	42	23	50
2382053021	46	53	16	31
2539199585	70	76	14	45
2662097003	58	74	15	52
2739907121	57	58	16	42
2775476404	52	36	18	39
2857946610	57	40	16	39
2950589658	51	40	14	35
2987390833	54	42	49	36
3131103033	50	54	37	34
3173757166	62	41	36	42
3250031541	46	24	30	30
3469728070	56	24	28	42
3672319062	51	26	28	40
3523242342	54	26	38	49
4180536954	100	35	46	56
4390129243	88	47	29	48
4545752925	76	27	36	38
4492359898	96	35	28	32
4565790908	83	29	100	40
4631462146	81	38	65	34
4757514054	94	45	49	35
4837221517	73	43	35	29
5049627766	65	30	33	26

require(GGally)
ggpairs(df_M1)

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require(zoo)
M1_lag1dif <- diff(zoo(M1_data$M1),lag=1,differences = 1,na.pad = TRUE)
faiz_data_lag1dif <- diff(zoo(faiz_data$Amount),lag=1,differences = 1,na.pad = TRUE)
enflasyon_data_lag1_dif <- diff(zoo(enflasyon_data$Amount),lag=1,differences = 1,na.pad = TRUE)

df_M1_lag1 <- cbind(M1_lag1dif, faiz_data_lag1dif, enflasyon_data_lag1_dif, darphane_data$Amount, altın_data$Amount)
ggpairs(df_M1_lag1)

Zorunlu paket yükleniyor: zoo


Attaching package: ‘zoo’


The following objects are masked from ‘package:data.table’:

    yearmon, yearqtr


The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric


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tslm_M1_1 <- tslm(M1_ts ~ faiz_ts + enflasyon_ts + darphane_ts + altın_ts)
tslm_M1_2 <- tslm(M1_ts ~ faiz_ts + enflasyon_ts + darphane_ts)
tslm_M1_3 <- tslm(M1_ts ~ enflasyon_ts + darphane_ts)
summary(tslm_M1_1)
summary(tslm_M1_2)
summary(tslm_M1_3)

Call:
tslm(formula = M1_ts ~ faiz_ts + enflasyon_ts + darphane_ts + 
    altın_ts)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.101e+09 -3.856e+08  6.162e+07  1.962e+08  2.554e+09 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -613195874  127466409  -4.811 4.53e-06 ***
faiz_ts        11854213    4671710   2.537   0.0125 *  
enflasyon_ts   23299524    5698982   4.088 8.00e-05 ***
darphane_ts    43165538    5579745   7.736 3.98e-12 ***
altın_ts        8265582    6046416   1.367   0.1742    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 662400000 on 117 degrees of freedom
Multiple R-squared:   0.75,	Adjusted R-squared:  0.7414 
F-statistic: 87.74 on 4 and 117 DF,  p-value: < 2.2e-16

Call:
tslm(formula = M1_ts ~ faiz_ts + enflasyon_ts + darphane_ts)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.066e+09 -3.793e+08  6.262e+07  1.913e+08  2.470e+09 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -606002904  127825722  -4.741 6.00e-06 ***
faiz_ts        14144476    4376990   3.232   0.0016 ** 
enflasyon_ts   26342318    5265606   5.003 1.99e-06 ***
darphane_ts    44715340    5483430   8.155 4.25e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 664800000 on 118 degrees of freedom
Multiple R-squared:  0.746,	Adjusted R-squared:  0.7395 
F-statistic: 115.5 on 3 and 118 DF,  p-value: < 2.2e-16

Call:
tslm(formula = M1_ts ~ enflasyon_ts + darphane_ts)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.502e+09 -2.761e+08 -3.878e+07  1.683e+08  2.571e+09 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -374645564  110016673  -3.405 0.000902 ***
enflasyon_ts   37138442    4228456   8.783 1.41e-14 ***
darphane_ts    52701533    5085463  10.363  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 690700000 on 119 degrees of freedom
Multiple R-squared:  0.7235,	Adjusted R-squared:  0.7189 
F-statistic: 155.7 on 2 and 119 DF,  p-value: < 2.2e-16

CV_data <- data.frame(rbind(CV(tslm_M1_1), CV(tslm_M1_2), CV(tslm_M1_3)))
CV_data

A data.frame: 3 × 5

CV	AIC	AICc	BIC	AdjR2
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
5.077549e+17	4962.855	4963.586	4979.679	0.7414411
4.935865e+17	4962.789	4963.306	4976.809	0.7395375
5.299412e+17	4971.134	4971.476	4982.350	0.7188692

autoplot(M1_ts, series = "Actual Data") + 
  autolayer(fitted(tslm_M1_1), series = "Model Result")

checkresiduals(tslm_M1_1)
checkresiduals(tslm_M1_2)
checkresiduals(tslm_M1_3)

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 91.051, df = 24, p-value = 9.651e-10

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 87.525, df = 24, p-value = 3.688e-09

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 80.951, df = 24, p-value = 4.289e-08

df_M1[,"Residuals"] <- as.numeric(residuals(tslm_M1_1))
ggplot(df_M1, aes(x = M1, y = Residuals)) + geom_point()
ggplot(df_M1, aes(x = fitted(tslm_M1_1), y = Residuals)) + geom_point()

Don't know how to automatically pick scale for object of type <ts>. Defaulting
to continuous.

Unemployment Rate Model:

autoplot(unemployment_rate_ts) + ggtitle("Unemployment Rate vs Time") + xlab("Year") + ylab("Unemployment Rate")

ggAcf(unemployment_rate_ts, lag.max = 120)
ggAcf(unemployment_rate_ts)

df_unemployment_rate <- cbind(unemployment_rate_data[,-1], iş_ilanları_data$Amount, enflasyon_data$Amount, işsizlik_sigortası_data$Amount, salgın_hastalık_data$Amount, işsizlik_data$Amount)
df_unemployment_rate

A data.frame: 122 × 6

Rate	iş_ilanları_data$Amount	enflasyon_data$Amount	işsizlik_sigortası_data$Amount	salgın_hastalık_data$Amount	işsizlik_data$Amount
<dbl>	<int>	<int>	<int>	<int>	<int>
10.5	45	9	14	7	18
10.5	46	5	15	3	22
9.4	40	4	13	3	16
8.8	40	5	13	4	18
8.4	39	5	12	4	17
9.0	45	5	14	3	19
9.7	44	5	12	3	19
10.1	46	3	12	5	17
10.1	51	3	14	4	20
10.9	47	4	12	4	18
10.4	52	4	15	3	21
10.8	50	5	13	2	21
11.9	52	9	16	1	24
11.7	55	6	14	3	22
10.4	51	6	12	4	22
9.6	46	5	13	2	20
8.9	44	7	13	3	21
9.6	55	9	12	3	23
10.0	52	7	13	2	21
10.0	56	4	12	2	21
10.3	53	4	10	3	20
10.5	58	4	11	3	21
10.2	53	6	13	3	24
10.9	54	9	14	3	24
11.5	55	13	17	4	40
11.0	62	5	16	2	38
10.1	58	5	12	2	34
9.1	55	5	12	3	32
9.0	55	5	12	3	28
10.1	60	5	13	3	29
⋮	⋮	⋮	⋮	⋮	⋮
11.1	64	17	5	28	26
10.7	57	25	6	6	25
10.9	51	38	7	3	25
11.3	48	56	7	6	35
12.1	56	66	9	6	43
11.4	59	38	7	5	39
11.4	58	42	8	5	34
10.6	48	53	7	5	34
10.1	52	76	7	7	28
9.7	60	74	7	5	34
10.6	57	58	6	5	39
9.8	59	36	8	3	36
9.9	59	40	7	4	33
9.9	57	40	7	4	31
9.9	53	42	6	8	30
10.4	51	54	9	13	39
10.3	56	41	8	10	42
10.7	55	24	6	10	34
10.2	56	24	8	6	34
10.0	45	26	10	6	31
8.8	47	26	6	4	30
9.0	49	35	10	6	38
9.7	60	47	7	6	38
9.2	55	27	6	7	34
8.9	57	35	7	13	35
8.2	49	29	6	11	33
8.8	47	38	6	8	31
8.9	44	45	7	16	36
9.8	49	43	7	29	42
9.7	45	30	10	9	38

require(GGally)
ggpairs(df_unemployment_rate)

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tslm_unemployment_rate_1 <- tslm(unemployment_rate_ts ~ iş_ilanları_ts + enflasyon_ts + salgın_hastalık_ts + işsizlik_sigortası_ts + işsizlik_ts)
tslm_unemployment_rate_2 <- tslm(unemployment_rate_ts ~ iş_ilanları_ts + enflasyon_ts + salgın_hastalık_ts + işsizlik_ts)
tslm_unemployment_rate_3 <- tslm(unemployment_rate_ts ~ iş_ilanları_ts + enflasyon_ts + işsizlik_ts)
summary(tslm_unemployment_rate_1)
summary(tslm_unemployment_rate_2)
summary(tslm_unemployment_rate_3)

Call:
tslm(formula = unemployment_rate_ts ~ iş_ilanları_ts + enflasyon_ts + 
    salgın_hastalık_ts + işsizlik_sigortası_ts + işsizlik_ts)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.17093 -0.80596 -0.08229  0.68503  3.06319 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)            6.108889   0.667855   9.147 2.36e-15 ***
iş_ilanları_ts         0.063310   0.008408   7.530 1.20e-11 ***
enflasyon_ts          -0.017956   0.008110  -2.214 0.028784 *  
salgın_hastalık_ts     0.023155   0.011965   1.935 0.055392 .  
işsizlik_sigortası_ts -0.016515   0.041892  -0.394 0.694140    
işsizlik_ts            0.046211   0.013285   3.478 0.000711 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.124 on 116 degrees of freedom
Multiple R-squared:  0.5313,	Adjusted R-squared:  0.5111 
F-statistic:  26.3 on 5 and 116 DF,  p-value: < 2.2e-16

Call:
tslm(formula = unemployment_rate_ts ~ iş_ilanları_ts + enflasyon_ts + 
    salgın_hastalık_ts + işsizlik_ts)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1160 -0.8259 -0.1021  0.6669  3.1330 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         5.936762   0.503535  11.790  < 2e-16 ***
iş_ilanları_ts      0.063780   0.008293   7.691 5.04e-12 ***
enflasyon_ts       -0.016136   0.006644  -2.429 0.016678 *  
salgın_hastalık_ts  0.023802   0.011809   2.016 0.046132 *  
işsizlik_ts         0.044248   0.012273   3.605 0.000459 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.12 on 117 degrees of freedom
Multiple R-squared:  0.5307,	Adjusted R-squared:  0.5147 
F-statistic: 33.08 on 4 and 117 DF,  p-value: < 2.2e-16

Call:
tslm(formula = unemployment_rate_ts ~ iş_ilanları_ts + enflasyon_ts + 
    işsizlik_ts)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0833 -0.8053 -0.0648  0.5990  3.1677 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     6.042571   0.507249  11.912  < 2e-16 ***
iş_ilanları_ts  0.057940   0.007871   7.361 2.66e-11 ***
enflasyon_ts   -0.018134   0.006655  -2.725  0.00741 ** 
işsizlik_ts     0.058272   0.010241   5.690 9.40e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.134 on 118 degrees of freedom
Multiple R-squared:  0.5144,	Adjusted R-squared:  0.5021 
F-statistic: 41.67 on 3 and 118 DF,  p-value: < 2.2e-16

CV_data <- data.frame(rbind(CV(tslm_unemployment_rate_1), CV(tslm_unemployment_rate_2), CV(tslm_unemployment_rate_3)))
CV_data

A data.frame: 3 × 5

CV	AIC	AICc	BIC	AdjR2
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1.429387	36.29801	37.28046	55.92616	0.5111465
1.383645	34.46135	35.19178	51.28548	0.5146754
1.310680	36.62576	37.14300	50.64587	0.5020789

autoplot(unemployment_rate_ts, series = "Actual Data") + 
  autolayer(fitted(tslm_unemployment_rate_2), series = "Model Result")

checkresiduals(tslm_unemployment_rate_1)
checkresiduals(tslm_unemployment_rate_2)
checkresiduals(tslm_unemployment_rate_3)

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 86.587, df = 24, p-value = 5.253e-09

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 86.204, df = 24, p-value = 6.069e-09

	Breusch-Godfrey test for serial correlation of order up to 24

data:  Residuals from Linear regression model
LM test = 86.535, df = 24, p-value = 5.358e-09

df_unemployment_rate[,"Residuals"] <- as.numeric(residuals(tslm_unemployment_rate_2))
ggplot(df_M1, aes(x = fitted(tslm_unemployment_rate_2), y = Residuals)) + geom_point()

Don't know how to automatically pick scale for object of type <ts>. Defaulting
to continuous.